code: def test_noise_augmentation(
      audio_channels, audio_length, noise_length
  ):
      try:
          opts = get_config()
          setattr(opts, "audio_augmentation.noise.levels", [-32])
          setattr(opts, "audio_augmentation.noise.cache_size", 1)
          setattr(opts, "audio_augmentation.noise.refresh_freq", 1)
          noise_file_dir = tempfile.mkdtemp()
          setattr(
              opts,
              "audio_augmentation.noise.files_dir",
              noise_file_dir,
          )
          dummy_noise = (
              (torch.rand((noise_length, 1)) - 0.5) * 2
          ).numpy()
          wav.write(
              os.path.join(noise_file_dir, "noise_sample1.wav"),
              data=dummy_noise,
              rate=16000,
          )
          augmenter = Noise(opts)
          dummy_audio = (
              torch.rand((audio_channels, audio_length)) - 0.5
          ) * 2
          data = {
              "samples": {"audio": dummy_audio},
              "metadata": {"audio_fps": 16000},
          }
          augmented = augmenter(data)
          assert (
              data["samples"]["audio"].shape
              == augmented["samples"]["audio"].shape
          )
          assert (
              augmented["samples"]["audio"] != dummy_audio
          ).float().sum() > 0.99 * dummy_audio.numel()
      finally:
          shutil.rmtree(noise_file_dir)
  
file_path: ml-cvnets/tests/transforms/test_audio.py
imports: import copy
  import os
  import shutil
  import tempfile
  import pytest
  import scipy.io.wavfile
  import torch
  from data.transforms.audio import AudioResample, Gain, LambdaAudio, MFCCs, Noise, Roll, SetFixedLength, StandardizeChannels
  from tests.configs import get_config
label_desc: Use image node_id 8 for calling a global function with example usage: test_noise_augmentation(audio_channels, audio_length, noise_length) without return types